You are not logged in.

Learning people movement model from multiple cameras for behaviour recognition

Nguyen, Nam T., Venkatesh, Svetha, West, Geoff A. W. and Bui, Hung H. 2004, Learning people movement model from multiple cameras for behaviour recognition. In Fred, Ana, Caelli, Terry, Duin, Robert P.W., Campilho, Aurélio and de Ridder, Dick (ed), Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 : proceedings, Springer-Verlag, Berlin, Germany, pp.315-324, doi: 10.1007/978-3-540-27868-9_33.

Attached Files
Name Description MIMEType Size Downloads

Title Learning people movement model from multiple cameras for behaviour recognition
Author(s) Nguyen, Nam T.
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
West, Geoff A. W.
Bui, Hung H.
Title of book Structural, syntactic, and statistical pattern recognition : joint IAPR international workshops SSPR 2004 and SPR 2004, Lisbon, Portugal, August 18-20, 2004 : proceedings
Editor(s) Fred, Ana
Caelli, Terry
Duin, Robert P.W.
Campilho, Aurélio
de Ridder, Dick
Publication date 2004
Series Lecture notes in computer science ; 3138
Chapter number 33
Total chapters 127
Start page 315
End page 324
Total pages 10
Publisher Springer-Verlag
Place of Publication Berlin, Germany
Keyword(s) behaviour
prediction
Abstract Hidden Markov mEmory Model
Summary In surveillance systems for monitoring people behaviours, it is important to build systems that can adapt to the signatures of people's tasks and movements in the environment. At the same time, it is important to cope with noisy observations produced by a set of cameras with possibly different characteristics. In previous work, we have implemented a distributed surveillance system designed for complex indoor environments [1]. The system uses the Abstract Hidden Markov mEmory Model (AHMEM) for modelling and specifying complex human behaviours that can take place in the environment. Given a sequence of observations from a set of cameras, the system employs approximate probabilistic inference to compute the likelihood of different possible behaviours in real-time. This paper describes the techniques that can be used to learn the different camera noise models and the human movement models to be used in this system. The system is able to monitor and classify people behaviours as data is being gathered, and we provide classification results showing the system is able to identify behaviours of people from their movement signatures.
ISBN 9783540225706
3540225706
ISSN 0302-9743
Language eng
DOI 10.1007/978-3-540-27868-9_33
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1.1 Book chapter
Copyright notice ©2004, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044663

Document type: Book Chapter
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 1 times in TR Web of Science
Scopus Citation Count Cited 6 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 300 Abstract Views, 1 File Downloads  -  Detailed Statistics
Created: Fri, 20 Apr 2012, 13:24:00 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.